Computer Vision Job Board - Posting Details

Title: Cross-Domain Recognition of Depression from Facial and Speech TraitsPosted: March 6, 2017
Company/Institution: École de technologie supérieure, Université du Québec
Location: Montreal, Canada

Description: Applications are invited for a funded PhD position in machine learning at the École de technologie supérieure (ETS), U. of Quebec, Montreal, Canada. The candidate will work under the supervision of Professors Eric Granger and Patrick Cardinal in the Laboratory for Imaging, Vision and Artificial Intelligence (LIVIA, see link below). The position is available immediately after the candidate passes ETS application requirements. Financial support is available for the project’s duration (maximum of 3-4 years). We are looking for highly motivated doctoral students, who are interested in performing cutting-edge research in multimodal fusion of face and speech traits captures in videos for affective computing applications, with a particular focus on deep learning (e.g, CNN and LSTM) architectures, domain adaptation and weakly-supervised learning. Prospective applicants should have: • Strong academic record with an excellent M.Sc. degree in computer science, applied mathematics, or electrical engineering, preferably with expertise in one or more of the following areas: machine learning, neural networks, computer vision and face recognition; • A good mathematical background; • Good programming skills in languages such as C, C++, Python and/or MATLAB. A prior publication in one of the major conferences or journals in computer vision/machine learning is not necessary but would be a very desirable.

Application Instructions: For consideration, please send a resume, names and contact details of two references, transcripts for under-graduate and graduate studies, and a link to a Masters thesis (as well as relevant publications if any) to: Prof. Eric Granger (Eric.Granger@etsmtl.ca); Prof. Patrick Cardinal (Patrick.Cardinal@etsmtl.ca) Laboratory for Imaging, Vision and Artificial Intelligence (LIVIA): http://www.etsmtl.ca/Unites-de-recherche/LIVIA/accueil?lang=en-CA